There are numerous prior art methods and apparatuses for automatic recognition and categorisation of elements and objects appearing in digital images. Such methods and apparatuses can be applied especially preferably in medical and diagnostic devices for the automatic analysis of body fluids such as urine or blood.
Furthermore, image recognition methods and apparatuses can be applied in all such technical fields, where objects of digital images are to be recognised and categorised. Such technical fields are for example, observation techniques by satellite or telescope, procession of information provided by outdoor security surveillance cameras or the implementation of user comfort functions in digital still and movie cameras.
In U.S. Pat. No. 5,830,141 an image processing method and apparatus are disclosed, which is adapted for the automatic detection of a predetermined type of cancerous area in an X-ray scan. The purpose of the known method and apparatus is the supporting of and contributing to the radiologist's decision making. In this known method, transformed images are made by various filters from the finished X-ray image, and then the original and transformed images are subjected to analysis by a neural network. The complete image or the separately identified one or more examination areas are analysed. In the method, the various image areas are classified in four types of categories in a way that the neural network is used to calculate for each pixel point the probability value that it belongs to a category. In this a way, practically four types of probability maps are generated from the image. Next, on the basis of analysing the probability maps, a decision is made about the category into which the given image area falls. If, in respect of a given image area, two or more categories emerge with a high probability, a decision about the category to be assigned to the image detail is made by the separate analysis and credibility assessment of the given probability map part. The disadvantage of the method is that regarding a given image detail, if several possible categories emerge, it does not rely on information other than the various probability maps, and therefore it is unable to make an analysis of the probabilities of each category in correlation with each other comprehensively for the image. Therefore, this known method and apparatus work with a relatively high error rate. A similar method is disclosed in Barbara P. E. et al: “Toward Automatic Phenotyping of Developing Embryos From Videos” (IEEE Transactions on Image Processing, IEEE Service Center, Piscataway, N.J., US, vol. 14, no. 9, 1 Sep. 2005 (2005-09-01), pages 1360-1371, XP011137594, ISSN: 1057-7149, DOI: 10.1109/TIP.2005.852470.
In U.S. Pat. No. 7,236,623 B2 an image recognition method and apparatus are disclosed for a urinalysis diagnostic system. In this method, the typical visual characteristics concerning the element categories appearing in the image are determined, and then in the light of these characteristics, the elements in the image are categorised by a multi-level neural network analysis. It is a disadvantage of this known approach that a multi-level neural network analysis exclusively follows the decision branching path based on the visual characteristics, and it is unsuitable for the global analysis and categorisation of the image elements.